In order to model the relations between the boundary and action content of temporal proposals, BC-GNN [1]} proposed a graph neural network where boundaries and content of proposals are taken as the nodes and edges of the graph, and their features are updated through graph operations. Then the updated edges and nodes are used to predict boundary probabilities and content confidence score to generate proposals. A2Net [2]} and AFSD [3]} visit the anchor-free mechanism, where the network predicts the distance to the temporal boundaries for each temporal location in the feature sequence. AFSD [3]} also proposes a novel boundary refinement strategy for precise temporal localization.
